Hi everyone,
I’m considering to make a career change into data analysis and recently completed the Google Data Analytics Certificate on Coursera. While it was a solid introduction, I found that it didn’t go very in-depth on tools like SQL, Excel, Tableau, or R.

My Coursera membership has expired. While I am open to signing up again I was curious if there are other websites you would recommend instead? I know its free on Youtube but I prefer a more structure learning course.

Thank you for any help you can provide!

I’m just starting to learn SQL and I’m looking for some solid websites or apps that are beginner-friendly. Ideally, I’d like something interactive or hands-on rather than just reading theory. I’m not aiming to become a full-on data engineer—just want to get comfortable writing queries, understanding databases, and maybe do some small projects.

Any recommendations for platforms (free or paid) that helped you when you were starting out? 

Thanks in advance

I’m curious to hear your thoughts on AI features in BI tools. I’ve been exploring options like Tableau and Power BI — especially their natural language query functions. Has anyone here actually used these? Are they true time-savers or more trouble than they’re worth?

For instance, Tableau’s Ask Data seems handy for quick visualizations, but I’ve heard it sometimes misses key filters, like specific years. Power BI’s Q&A feels a bit more responsive, especially for things like “sales by region in 2022.” I also came across FineBI’s new AI Q&A feature — seems beginner-friendly for self-service data prep. Anyone given it a try? How does it stack up?

Would love to know — are these AI features genuinely helpful, or do they still require too much oversight? And are there any other BI tools with solid AI capabilities you’d recommend?

Looking forward to your insights!

Hi everyone,

I’m currently an undergraduate student in Data Science, actively working toward becoming a data scientist. So far, I’ve built a foundation with basic machine learning models using libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and some PyTorch. I’ve also explored LLMs by working with pre-trained models through Hugging Face and LangChain. Lately, I’ve been diving into more advanced ML and deep learning concepts, setting up CI/CD pipelines, and learning backend development for ML using FastAPI and Flask.

Despite experimenting with this wide range of tools and technologies, I still find myself unclear about what companies actually expect from data scientists—both at junior and senior levels. What tech stack should I focus on? Which trends and skills are truly valued in the industry?

As a student, it’s hard to get a clear answer on this. Could someone with experience in the field help clarify what companies are really looking for in data scientists today?

Thanks in advance!

In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?
What indicators or monitoring strategies help you decide when updates are needed?